Communication system and methods using very large multiple-in multiple-out (MIMO) antenna systems with extremely large class of fast unitary transformations

ABSTRACT

An apparatus includes a first communication device with multiple antennas, operably coupled to a processor and configured to access a codebook of transformation matrices. The processor generates a set of symbols based on an incoming data, and applies a permutation to each of the symbols to produce a set of permuted symbols. The processor transforms each of the permuted symbols based on at least one primitive transformation matrix, to produce a set of transformed symbols. The processor applies, to each of the transformed symbols, a precode matrix selected from the codebook of transformation matrices to produce a set of precoded symbols. The codebook of transformation matrices is accessible to a second communication device. The processor sends a signal to cause transmission, to the second communication device, of multiple signals, each representing a precoded symbol from the set of precoded symbols, each of the signals transmitted using a unique antenna from the plurality of antennas.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/580,722, filed Sep. 24, 2019, and titled “COMMUNICATION SYSTEM ANDMETHODS USING VERY LARGE MULTIPLE-IN MULTIPLE-OUT (MIMO) ANTENNA SYSTEMSWITH EXTREMELY LARGE CLASS OF FAST UNITARY TRANSFORMATIONS,” which isrelated to U.S. Pat. No. 10,020,839, issued on Jul. 10, 2018 and titled“RELIABLE ORTHOGONAL SPREADING CODES IN WIRELESS COMMUNICATIONS,” and toU.S. patent application Ser. No. 16/459,262, filed on Jul. 1, 2019 andtitled “COMMUNICATION SYSTEM AND METHOD USING LAYERED CONSTRUCTION OFARBITRARY UNITARY MATRICES,” and to U.S. patent application Ser. No.16/527,240, filed on Jul. 31, 2019 and titled “COMMUNICATION SYSTEM ANDMETHOD USING UNITARY BRAID DIVISIONAL MULTIPLEXING (UBDM) WITH PHYSICALLAYER SECURITY (PLS),” the disclosures of each of which are incorporatedby reference herein in their entireties for all purposes.

STATEMENT REGARDING FEDERAL GOVERNMENT INTEREST

This United States Government holds a nonexclusive, irrevocable,royalty-free license in the invention with power to grant licenses forall United States Government purposes.

TECHNICAL FIELD

This description relates to systems and methods for transmittingwireless signals for electronic communications and, in particular, toincreasing the data rate of, and reducing the computational complexityof, wireless communications performed via a very large number ofantennas.

BACKGROUND

In multiple access communications, multiple user devices transmitsignals over a given communication channel to a receiver. These signalsare superimposed, forming a combined signal that propagates over thatcommunication channel. The receiver then performs a separation operationon the combined signal to recover one or more individual signals fromthe combined signal. For example, each user device may be a cell phonebelonging to a different user and the receiver may be a cell tower. Byseparating signals transmitted by different user devices, the differentuser devices may share the same communication channel withoutinterference.

A transmitter may transmit different symbols by varying a state of acarrier or subcarrier, such as by varying an amplitude, phase and/orfrequency of the carrier. Each symbol may represent one or more bits.These symbols can each be mapped to a discrete value in the complexplane, thus producing Quadrature Amplitude Modulation, or by assigningeach symbol to a discrete frequency, producing Frequency Shift Keying.The symbols are then sampled at the Nyquist rate, which is at leasttwice the symbol transmission rate. The resulting signal is converted toanalog through a digital to analog converter, and then translated up tothe carrier frequency for transmission. When different user devices sendsymbols at the same time over the communication channel, the sine wavesrepresented by those symbols are superimposed to form a combined signalthat is received at the receiver.

SUMMARY

An apparatus includes a first communication device with multipleantennas, operably coupled to a processor and configured to access acodebook of transformation matrices. The processor generates a set ofsymbols based on an incoming data, and applies a permutation to each ofthe symbols to produce a set of permuted symbols. The processortransforms each of the permuted symbols based on at least one primitivetransformation matrix, to produce a set of transformed symbols. Theprocessor applies, to each of the transformed symbols, a precode matrixselected from the codebook of transformation matrices to produce a setof precoded symbols. The codebook of transformation matrices isaccessible to a second communication device. The processor sends asignal to cause transmission, to the second communication device, ofmultiple signals, each representing a precoded symbol from the set ofprecoded symbols, each of the signals transmitted using a unique antennafrom the plurality of antennas.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features will beapparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example very large multiple-inmultiple-out (MIMO) communications system for fast spatial unitarytransformation, according to an embodiment.

FIG. 2 is a flowchart illustrating a first example method for performingfast spatial unitary transformation, including generating andtransmitting precoded symbols, according to an embodiment.

FIG. 3 is a flowchart illustrating a second example method forperforming fast spatial unitary transformation, including generating andtransmitting precoded symbols, according to an embodiment.

FIG. 4 is a flowchart illustrating an example communication method,including a singular value decomposition and generating transformedsignals, according to an embodiment.

FIG. 5 is a flowchart illustrating a method of communication using alayered construction of an arbitrary matrix, according to an embodiment.

FIG. 6 is a diagram illustrating discrete Fourier Transform (DFT) of avector b=(b₀, b₁, . . . b_(N-1)).

FIG. 7 is a schematic of a system for communication using layeredconstruction of unitary matrices, according to an embodiment.

DETAILED DESCRIPTION

Some multiple-in multiple-out (MIMO) communications systems includetransmitters and receivers that apply a unitary transformation acrossmultiple spatial antennas, with the specific unitary matrices appliedbeing determined by a processor, based on the communication channel(e.g., a physical transmission medium over which signals are sent, suchas free space, having multi-path and other environmentalcharacteristics). The unitary matrices can be selected from a codebookof essentially random unitary matrices. Such approaches are adequate formost known MIMO systems because most known MIMO systems include arelatively small number of antennas (2-4 antennas is common). As datarequirements and the demand for spatial diversity and spatialmultiplexing increase, however, the number of desired communicationchannels increases. As a result, the number of associated unitarypre-multiplications and post-multiplications performed at thetransmitter (Tx) and receiver (Rx) can also increase. Since the numberof matrix multiplications increases as O(N²), this increase incomplexity can become computationally expensive/prohibitive. The “O” inthe expression O(N²) is “Big O” mathematical notation, indicating theapproximate value that the relevant function/operation approaches.

Embodiments set forth herein can achieve improved-efficiency MIMOcommunications through the construction of codebooks of fast unitarymatrices and their application to spatial diversity/MIMO systems forMIMO-precoding. In U.S. patent application Ser. No. 16/459,262, filed onJul. 1, 2019 and titled “COMMUNICATION SYSTEM AND METHOD USING LAYEREDCONSTRUCTION OF ARBITRARY UNITARY MATRICES,” a technique is discussedfor applying an extremely large class of “fast” unitary matrices fortransforming modulated symbols in the frequency domain (e.g., replacingan inverse Fast Fourier transform (iFFT)), prior to transmission of thesymbols. An “extremely large class” of fast unitary matrices can referto a class including between 2⁴⁰⁰ and 2^(20,000) (e.g., 2^(8,000)) fastunitary matrices. Systems and methods of the present disclosure extendthe construction and implementation of “fast” unitary operators outsidethe context of the frequency domain, for orthogonal frequency-divisionmultiplexing (OFDM) systems. OFDM is a method of encoding digital dataon multiple carrier frequencies.

Because fast unitary matrices are relatively dense in the full unitarygroup (i.e., the full set of possible unitary matrices), it is possibleto design a suitable codebook of potential channel matrices out of thefast unitary matrices, and, in turn, to engineer much larger MIMOsystems than would otherwise be possible. Embodiments set forth hereininclude the construction of channel matrix codebooks out of fast unitarymatrices (also referred to herein as “operators” or “transformations”),such that much larger MIMO systems can be designed without thecomputational complexity of naive unitary spatial transformations. Asused herein, a “fast” or “high-speed” transformation refers to one thatcan be performed using work that is on the order of no worse than O(Nlog N) or O(K log K) floating point operations (e.g., given an N×Kmatrix).

MIMO systems typically employ a process referred to as “pre-coding.”Details about MIMO pre-coding can be found, for example, in “PracticalPhysical Layer Security Schemes for MIMO-OFDM Systems Using PrecodingMatrix Indices” by Wu, Lan, Yeh, Lee, and Cheng, published in IEEEJournal on Selected Areas in Communications (Vol. 31, Issue 9, September2013), the entire contents of which are herein incorporated by referencein their entirety for all purposes. To illustrate, consider that Aliceand Bob (a pair of communicating entities) agree to a “codebook” ofunitary matrices (i.e., a stored collection of unitary matrices)available for use during communications. Alice transmits a trainingsequence to Bob, and Bob can determine the channel matrix H based on thetraining sequence. From channel matrix H, Bob can use the generalizedchannel capacity to determine which unitary matrix in the codebookmaximizes capacity, and transmit only the bits labeling that matrix backto Alice. Alice can then pre-multiply, or “pre-code,” every baud shetransmits from that point on with the appropriate unitary matrix fromthe codebook). Bob then multiplies by the remaining unitary singularmatrix, and scales out the singular values. Matrices in the codebook canbe selected pseudo-randomly. An efficiency benefit can be realized usingpseudo-randomly selected matrices (i.e., without identifying/using theexact matrices), given the associated reduction in the volume of bitsbeing transmitted.

The pre-coded/pre-multiplied unitary matrices are applied across space,not across frequency or time. In other words, if t antennas are alltransmitting at the same time, and the desired symbols to be transmittedare b=(b₁, . . . , b_(t)), and the precode matrix is F, then the firstantenna actually transmits

${\sum\limits_{n = 1}^{t}\;{F_{1n}b_{n}}},$the second antenna transmits

${\sum\limits_{n = 1}^{t}\;{F_{2n}b_{n}}},$and so on. The foregoing illustrates the application of a spatialunitary matrix.

A similar procedure can be performed in conjunction with the unitarymatrices in fast Unitary Braid Divisional Multiplexing (fUBDM)(discussed in detail in U.S. patent application Ser. No. 16/527,240,filed on Jul. 31, 2019 and titled “Communication System and Method UsingUnitary Braid Divisional Multiplexing (UBMD) with Physical LayerSecurity (PLS),” incorporated herein by reference). For example, supposethat the symbols to be transmitted on the n^(th) antenna areb ^(n)=(b ₁ ^(n) , . . . ,b _(N) ^(n)),and the fUBDM unitary on the n^(th) antenna is A^(n). Then thetransmitter first computess ^(n) =A ^(n) b ^(n)for every n. The symbol s ^(n) are what are actually being transmittedon the n^(th) antenna. Then, when the receiver is ready to transmit thet values s ^(n) for n=1, . . . ,t, the transmitter computes the valuesFs ^(n) =FA ^(n) b ^(n)and transmits those.

Consider the following example. Suppose that N=2 and t=2, and the firstantenna uses the matrix A₁ and the second antenna uses the matrix A₂,where

$\begin{matrix}{{A_{1} = {\frac{1}{\sqrt{2}}\begin{pmatrix}1 & 1 \\{- 1} & 1\end{pmatrix}}},} & \left( {0.0{.1}} \right) \\{and} & \; \\{A_{2} = {\frac{1}{2}{\begin{pmatrix}1 & \sqrt{3} \\\sqrt{3} & {- 1}\end{pmatrix}.}}} & \left( {0.0{.2}} \right)\end{matrix}$

Consider also that the space-time matrix is:

$\begin{matrix}{F = {\frac{1}{2}{\begin{pmatrix}\sqrt{3} & {- 1} \\1 & \sqrt{3}\end{pmatrix}.}}} & (0.03)\end{matrix}$

Next, suppose that the first antenna is going to transmit the symbols(b₁ ¹, b₂ ¹), and the second antenna is going to transmit (b₁ ², b₂ ²).First, both antennas spread their symbols, such that the first antennacomputes

$\begin{matrix}{{{A_{1}{\overset{\_}{b}}^{1}} = {{\frac{1}{\sqrt{2}}\begin{pmatrix}1 & 1 \\{- 1} & 1\end{pmatrix}\begin{pmatrix}b_{1}^{1} \\b_{2}^{1}\end{pmatrix}} = {\frac{1}{\sqrt{2}}\begin{pmatrix}{b_{1}^{1} + b_{2}^{1}} \\{{- b_{1}^{1}} + b_{2}^{1}}\end{pmatrix}}}},} & \left( {0.0{.4}} \right)\end{matrix}$and the second antenna computes

$\begin{matrix}{{\frac{1}{2}\begin{pmatrix}1 & \sqrt{3} \\\sqrt{3} & {- 1}\end{pmatrix}\begin{pmatrix}b_{1}^{1} \\b_{2}^{2}\end{pmatrix}} = {\frac{1}{2}{\begin{pmatrix}{b_{1}^{2} + {\sqrt{3}b_{2}^{2}}} \\{{\sqrt{3}b_{1}^{2}} - b_{2}^{2}}\end{pmatrix}.}}} & \left( {0.0{.5}} \right)\end{matrix}$

When it is time to transmit, the antennas will apply the spatial unitaryacross the components. If the spatial unitary F was the identity matrix,then at the first time slot the first antenna would transmit

${\frac{1}{\sqrt{2}}\left( {b_{1}^{1} + b_{2}^{1}} \right)},$and the second antenna would simultaneously transmit

$\frac{1}{2}{\left( {b_{1}^{2} + {\sqrt{3}b_{2}^{2}}} \right).}$

Because F is not the identity matrix, however, for the first time slotthe transmitters will compute:

$\begin{matrix}{{F\begin{pmatrix}{\frac{1}{\sqrt{2}}\left( {b_{1}^{1} + b_{2}^{1}} \right)} \\{\frac{1}{2}\left( {b_{1}^{2} + {\sqrt{3}b_{2}^{2}}} \right)}\end{pmatrix}} = {{\frac{1}{2}\begin{pmatrix}\sqrt{3} & {- 1} \\1 & \sqrt{3}\end{pmatrix}\begin{pmatrix}{\frac{1}{\sqrt{2}}\left( {b_{1}^{1} + b_{2}^{1}} \right)} \\{\frac{1}{2}\left( {b_{1}^{2} + {\sqrt{3}b_{2}^{2}}} \right)}\end{pmatrix}} = {\frac{1}{2}{\begin{pmatrix}{{\frac{\sqrt{3}}{\sqrt{2}}\left( {b_{1}^{1} + b_{2}^{1}} \right)} - {\frac{1}{2}\left( {b_{1}^{2} + {\sqrt{3}b_{2}^{2}}} \right)}} \\{{\frac{1}{\sqrt{2}}\left( {b_{1}^{1} + b_{2}^{1}} \right)} + {\frac{\sqrt{3}}{2}\left( {b_{1}^{2} + {\sqrt{3}b_{2}^{2}}} \right)}}\end{pmatrix}.}}}} & \left( {0.0{.6}} \right)\end{matrix}$

The first antenna transmits the first value

${{{- \frac{\sqrt{3}}{\sqrt{2}}}\left( {b_{1}^{1} + b_{2}^{1}} \right)} - {\frac{1}{2}\left( {b_{1}^{2} + {\sqrt{3}b_{2}^{2}}} \right)}},$and the second antenna simultaneously transmits the second value

${\frac{1}{\sqrt{2}}\left( {b_{1}^{1} + b_{2}^{1}} \right)} + {\frac{\sqrt{3}}{2}{\left( {b_{1}^{2} + {\sqrt{3}b_{2}^{2}}} \right).}}$

Then, at the second time slot, the transmitter computes

$\begin{matrix}{{F\begin{pmatrix}{\frac{1}{\sqrt{2}}\left( {{- b_{1}^{1}} + b_{2}^{1}} \right)} \\{\frac{1}{2}\left( {{\sqrt{3}b_{1}^{2}} - b_{2}^{2}} \right)}\end{pmatrix}} = \ldots} & \left( {0.0{.7}} \right)\end{matrix}$The values at (0.0.7) are the two values that the first transmitter andthe second transmitter will transmit, respectively, simultaneouslyduring the second time slot.

Once these values are transmitted through a communication channel, theeffect of F will be removed by the communication channel. Thisillustrates the reason this process is called “precoding,” as itinvolves the application of the inverse of at least a portion of whatthe communication channel is going to do. When the receiver receives thetransmitted signals, there will be no need to remove the precodingportions, because the communication channel has effectively removedthem. The receiver will then scale out the singular values and thenremove the other singular vectors, then apply the inverse of thegenerator matrices A₁ and A₂.

An example of the scaling out of the singular values is as follows: Inresponse to a signal “T” being transmitted, the receiver receives HT,where “H” represents the channel matrix. If the singular valuedecomposition of H is H=BDA^(t) (where the t superscript indicatesconjugate transpose), then the receiver receives (BDA^(t))T. If T wasselected to be Ab, where A is the same unitary as in the channel(similar to matrix “F” in the preceding discussion), and b is thetransmitted sequence, then the receiver receives (BDA^(t))Ab=BDb. If thereceiver then multiplies BDb by the conjugate transpose of B, the resultis B^(t) B Db=Db, which is the transmitted sequence b multiplied by adiagonal matrix D having all non-negative values, the diagonal values ofD being the singular values. As such, “scaling out the singular values”refers to dividing each component of Db by the singular values. Or,equivalently, “scaling out the singular values” refers to multiplying Dbby the inverse of D (which can be denoted by D⁻¹). As a result, thetransmitter obtains D⁻¹Db=b, which is the transmitted sequence.

A significant challenge with MIMO systems is that as the number ofantennas increases, the complexity of matrix multiplications (such asthose discussed above) grows with O(t²) for the transmitter and O(r²)for the receiver. Many known practical MIMO systems are relatively small(e.g., 2-4 antennas), however as systems and data rate requirementsgrow, known methods will cease to be sufficient. The general inabilityto computationally handle the unitary transformation for a largerantenna array will be prohibitive for growth in these systems.

Embodiments set forth herein address the foregoing challenges byleveraging UBDM and the associated large class of unitary matrices thatcan be applied in a fast manner. If the codebooks are selected from theset of “fast” matrices, then the complexity of a MIMO system will growwith O(t log t) for the transmitter and O(r log r) for the receiver,thus representing a drastic improvement over the current state of theart.

Application areas in which embodiments of the present disclosure areexpected to be of significant value are Internet of Things (IoT) and“Massive” MIMO systems. As IoT continues to grow, there will be more andmore devices, all vying for bandwidth. Because the devices willgenerally be very small, very low power, very low complexity devices,spatial diversity alone will be insufficient for achieving higher datarates (e.g., it may not be possible to successfully increase bandwidthand/or the power of the transmission). With the fast unitary matricesset forth herein, by contrast, systems effective for increasingtransmission bandwidth and/or power of the transmission can beimplemented, in a reliable and cost-effective manner. Moreover, in someembodiments system designers can use one or more of: standard timedivision multiplexing, frequency division multiplexing, code divisionmultiplexing (e.g., via the Code Division Multiple Access (CDMA) featureof UBDM), and spatial multiplexing (e.g., due to the reduction in MIMOpre-coding complexity due to the fast unitary matrices) during systemdesign, resulting in improved design flexibility. Alternatively or inaddition, when using UBDM, designers can omit the logic/chip settypically used for standard encryption, saving significant power draw,battery life, delay and latency in the network, physical space on thechip, and all of the overhead associated with encryption. Alternativelyor in addition, the reduced Peak-to-Average Power Ratio (PAPR) in UBDM(as compared with OFDM) can increase battery life significantly.Alternatively or in addition, with UBDM, faster key exchange can beachieved with fewer computational resources than traditional public keyalgorithms. The Direct Sequence Spread Spectrum (DSSS) feature of UBDMcan also provide a central hub that constantly reallocates codes amongdifferent users depending on desired data rate/bandwidth usage.

Embodiments set forth herein are also compatible with “Massive MIMO”systems (i.e., systems whose main application is for the “last mile”problem of achieving desired data rates within “fiber to the home”services, such as Verizon® Fios®). A Massive MIMO system typicallyoperates at millimeter wave center frequencies, have enormous spectralbandwidths (on the order of GHz), and exploit enormous spatial/MIMOdiversity (on the order of r=1,000-10,000 transmit antennas). Althoughsuch a configuration multiplies the capacity by a factor of1,000-10,000, the computational complexity of such a system (requiringat O(1,000²)=O(1,000,000) on the low end) renders it impractical. Byusing the unitary matrix construction from fUBDM according toembodiments set forth herein, practical Massive MIMO systems can berealized.

System Overview

FIG. 1 is a block diagram illustrating an example very large (e.g.,1,000-10,000 transmit antennas) multiple-in multiple-out (MIMO)communications system for fast spatial unitary transformation, accordingto an embodiment. As shown in FIG. 1, a system 100 includes a firstcommunication device 120 and a second communication device 150. Thefirst communication device 120 includes processing circuitry 122,transceiver circuitry 146, antennas 148 (which may be large in number),and non-transitory processor-readable memory 124. Similarly, the secondcommunication device 150 includes processing circuitry 152, transceivercircuitry 176, antennas 178 (which may be large in number), andnon-transitory processor-readable memory 154. The memory 124 of thefirst communication device 120 can store one or more of: a codebook oftransformation matrices 126, symbols 128, transformed symbols 130,permutations 132, primitive transformation matrices 134, permutedsymbols 136, signals 138, precode matrices 140, unitary matrices 142,and layers 144. Similarly, the memory 154 of the second communicationdevice 150 can store one or more of: a codebook of transformationmatrices 156, symbols 158, transformed symbols 160, permutations 162,primitive transformation matrices 164, permuted symbols 166, signals168, precode matrices 170, unitary matrices 172, and layers 174. Theantennas 148 and/or the antennas 178 can be configured to performMultiple Input Multiple Output (MIMO) operations.

Each of the memories 124 and 154 can store instructions, readable by theassociated processing circuitry (122 and 152, respectively) to performmethod steps, such as those shown and described with reference to FIGS.2-5 below. Alternatively or in addition, instructions and/or data (e.g.,a codebook of transformation matrices 126, symbols 128, transformedsymbols 130, permutations 132, primitive transformation matrices 134,permuted symbols 136, signals 138, precode matrices 140, unitarymatrices 142, and layers 144) can be stored in media 112 and/or 114 andaccessible to the first communication device 120 and/or the secondcommunication device 150, respectively.

FIG. 2 is a flowchart illustrating a first example method for performingfast spatial unitary transformation, including generating andtransmitting precoded symbols, according to an embodiment. The method200 can be implemented, for example, using the MIMO communicationssystem 100 of FIG. 1. As shown in FIG. 2, the method 200 includesgenerating a set of symbols, at 210, based on an incoming data (i.e.,any input data stream, which can include packets, which can include datathat may or may not be serialized, etc.), and apply a permutation toeach symbol from the set of symbols, at 212, to produce a set ofpermuted symbols. At 214, each permuted symbol from the set of permutedsymbols is transformed based on at least one primitive transformationmatrix, to produce a set of transformed symbols. A precode matrixselected (e.g., pseudo-randomly) from the codebook of transformationmatrices is applied, at 216, to each transformed symbol from the set oftransformed symbols to produce a set of precoded symbols. The codebookof transformation matrices is accessible to a second communicationdevice, and optionally does not include a frequency-domaintransformation or a time-domain transformation. The codebook oftransformation matrices can be configured for use in at least one of:time division multiplexing, frequency division multiplexing, codedivision multiplexing, or spatial multiplexing. At 218, a signal is sentto cause transmission, to the second communication device, of multiplesignals, each signal from the multiple signals representing a precodedsymbol from the set of precoded symbols, each signal from the multiplesignals transmitted using a unique antenna from the set of antennas. Themultiple signals can be sent via a communication channel that applies achannel transformation to the plurality of signals such that the precodematrix is removed. In some implementations, the signal to causetransmission of the multiple signals does not cause transmission of anyof the precode matrices and/or does not cause transmission of thecodebook of transformation matrices.

In some embodiments, the method 200 also includes generating thecodebook of precode matrices by decomposing a unitary transformationmatrix into a plurality of layers, each layer from the plurality oflayers including a permutation and a primitive transformation matrix.Alternatively or in addition, the multiple antennas are a first set ofantennas and the second communication device includes a second set ofantennas, the first communication device and the second communicationdevice configured to perform MIMO operations. The first set of antennascan include T antennas and the second set of antennas can include Rantennas, the MIMO operations having an associated computational cost ofO(T log₂ T) arithmetic operations for the first communication device,and the MIMO operations having an associated cost of O(R log₂ R)arithmetic operations for the second communication device.

FIG. 3 is a flowchart illustrating a second example method forperforming fast spatial unitary transformation, including generating andtransmitting precoded symbols, according to an embodiment. The method300 can be implemented, for example, using the MIMO communicationssystem 100 of FIG. 1. As shown in FIG. 3, the method 300 includesgenerating, at 310, a set of symbols based on an incoming data, anddecomposing, at 312, each unitary matrix from a plurality of unitarymatrices of the codebook of unitary matrices into an associated set oflayers. For each unitary matrix from the plurality of unitary matrices,each layer from the plurality of layers associated with that unitarymatrix can include a permutation and a primitive transformation matrix.At 314, at least one layer from an associated unitary matrix from theplurality of unitary matrices is applied to each symbol from the set ofsymbols, to generate a set of transformed symbols. At 316, a precodematrix selected from the codebook of unitary matrices is applied to eachtransformed symbol from the set of transformed symbols, to produce a setof precoded symbols. A signal is sent at 318 to cause transmission, tothe second communication device, of multiple signals. Each signal fromthe multiple signals represents a precoded symbol from the set ofprecoded symbols. Each signal from the multiple signals is transmittedusing a unique antenna from a set of multiple antennas. In someimplementations, the signal to cause transmission of the multiplesignals does not cause transmission of any of the precode matricesand/or does not cause transmission of the codebook of transformationmatrices.

In some embodiments, the set of multiple antennas is a first set ofantennas, and the second communication device includes a second set ofantennas, the first communication device and the second communicationdevice configured to perform MIMO operations. The first plurality ofantennas can include T antennas and the second plurality of antennas caninclude R antennas. The MMO operations can have an associatedcomputational cost of O(T log₂ T) arithmetic operations for the firstcommunication device, and the MIMO operations can have an associatedcomputational cost of O(R log₂ R) arithmetic operations for the secondcommunication device.

FIG. 4 is a flowchart illustrating an example communication method,including a singular value decomposition and generating transformedsignals, according to an embodiment. The method 400 can be implemented,for example, using the MIMO communications system 100 of FIG. 1. Asshown in FIG. 4, the method 400 includes receiving, at 410, at an arrayof antennas of a communication device and via a communication channel,multiple signals. Each signal from the multiple signals representstransformed symbols from a first set of transformed symbols. At 412, asingular value decomposition is performed, at the communication device,of a representation of the communication channel to identify a leftsingular vector of the communication channel and a right singular vectorof the communication channel. The singular value decomposition of an m×nreal or complex matrix M is a factorization of the form UΣV*, where U isan m×m real or complex unitary matrix, Σ is an m×n rectangular diagonalmatrix with non-negative real numbers on the diagonal, and V is an n×nreal or complex unitary matrix. The diagonal entries σ_(i) of Σ areknown as singular values of M. The columns of U and the column of V arecalled the left-singular vectors and right-singular vectors of M,respectively. At 414, the left singular vector and the right singularvector are removed from the first set of transformed symbols to generatea second set of transformed symbols. At least one message associatedwith the plurality of signals is identified at 416 by querying acodebook of transformation matrices based on the second plurality oftransformed symbols. Optionally, the codebook of transformation matricesdoes not include a frequency-domain transformation or a time-domaintransformation.

In some embodiments, the method 400 also includes decomposing a unitarytransformation matrix into multiple layers to produce the codebook oftransformation matrices. Each layer from the multiple layers can includea permutation and a primitive transformation matrix. Alternatively or inaddition, the array of antennas is a first array of antennas, and thesecond communication device includes a second array of antennas, withthe first communication device and the second communication deviceconfigured to perform MIMO operations. The first array of antennas caninclude R antennas and the second array of antennas can include Tantennas. The MIMO operations can have an associated computational costof O(R log₂ R) arithmetic operations for the first communication device,and the MIMO operations can have an associated computational cost of O(Tlog₂ T) arithmetic operations for the second communication device.

Example Fast Unitary Transformations—System and Methods

FIG. 5 is a flowchart illustrating a method of communication using alayered construction of an arbitrary matrix, according to an embodiment.The method 500 includes, at 510, generating, via a first processor of afirst compute device, a plurality of symbols. The method 500 alsoincludes, at 520, applying an arbitrary transformation of size N×N toeach symbol from the plurality of symbols to produce a plurality oftransformed symbols, where N is a positive integer. The arbitrarytransformation includes an iterative process (e.g., including multiplelayers), and each iteration includes: 1) a permutation followed by 2) anapplication of at least one primitive transformation matrix of size M×M,where M is a positive integer having a value smaller than or equal to N.

At 530, a signal representing the plurality of transformed symbols issent to a plurality of transmitters, which transmits a signalrepresenting the plurality of transformed symbols to a plurality ofreceivers. The method 500 also includes, at 540, sending a signalrepresenting the arbitrary transformation to a second compute device fortransmission of the arbitrary transformation to the plurality of signalreceivers prior to transmission of the plurality of transformed symbols,for recovery of the plurality of symbols at the plurality of signalreceivers.

In some embodiments, the plurality of signal receivers includes aplurality of antenna arrays, and the plurality of signal receivers andthe plurality of signal transmitters are configured to perform MultipleInput Multiple Output (MIMO) operations. In some embodiments, thearbitrary transformation includes a unitary transformation. In someembodiments, the arbitrary transformation includes one of a Fouriertransform, a Walsh transform, a Haar transform, a slant transform, or aToeplitz transform.

In some embodiments, each primitive transformation matrix from the atleast one primitive transformation matrix has a dimension (e.g., alength) with a magnitude of 2, and a number of iterations of theiterative process is log₂N. In some embodiments, any other appropriatelengths can be used for the primitive transformation matrix. Forexample, the primitive transformation matrix can have a length greaterthan 2 (e.g., 3, 4, 5, etc.). In some embodiments, the primitivetransformation matrix includes a plurality of smaller matrices havingdiverse dimensions. For example, the primitive transformation matrix caninclude block-U(m) matrices, where m can be different values within asingle layer or between different layers.

The fast matrix operations in the method 500 (e.g., 520) can be examinedin more detail with reference to Discrete Fourier Transform (DFT).Without being bound by any particular theory or mode of operation, theDFT of a vector b=(b₀, b₁, . . . ; b_(N-1)), denoted B, with componentsB_(k), can be given by:

$\begin{matrix}{B_{k} = {\sum\limits_{n = 0}^{N - 1}{b_{n}\omega_{N}^{nk}}}} & (18) \\{{{where}\mspace{14mu}\omega_{N}} = {e^{\frac{2\pi\; i}{N}}.}} & \;\end{matrix}$

Generally, a DFT involves N² multiplies when carried out using naivematrix multiplication, as illustrated by Equation (18). The roots ofunity ω_(N), however, have a set of symmetries that can reduce thenumber of multiplications. To this end, the sum in Equation (18) can beseparated into even and odd terms, as (assuming for now that N is amultiple of 2):

$\begin{matrix}{B_{k} = {{{\sum\limits_{n = 0}^{\frac{N}{2} - 1}{b_{2n}\omega_{N}^{2{nk}}}} + {\sum\limits_{n = 0}^{\frac{N}{2} - 1}{b_{{2n} + 1}\omega_{N}^{{({{2n} + 1})}k}}}} = {{\sum\limits_{n = 0}^{\frac{N}{2} - 1}{b_{2n}\omega_{N}^{2{nk}}}} + {\omega_{N}^{k}{\sum\limits_{n = 0}^{\frac{N}{2} - 1}{b_{{2n} + 1}\omega_{N}^{2{nk}}}}}}}} & (19)\end{matrix}$

In addition:

$\begin{matrix}{\omega_{N}^{2{nk}} = {e^{\frac{2\pi\; i\; 2\;{nk}}{N}} = {e^{\frac{2\pi\;{ink}}{N/2}} = {\omega_{N/2}^{n}k}}}} & (20)\end{matrix}$So B_(k) can be written as:

$\begin{matrix}{B_{k} = {{\sum\limits_{n = 0}^{\frac{N}{2} - 1}{b_{2n}\omega_{N/2}^{2{nk}}}} + {\omega_{N}^{k}{\sum\limits_{n = 0}^{\frac{N}{2} - 1}{b_{{2n} + 1}\omega_{N/2}^{2{nk}}}}}}} & (21)\end{matrix}$Now k runs over twice the range of n. But consider the follow equation:

$\begin{matrix}{\omega_{N/2}^{n{({\frac{N}{2} + k})}} = {e^{\frac{2\pi\;{{in}({\frac{N}{2} + k})}}{N/2}} = {{e^{2{\pi{in}}}e^{\frac{2\pi\;{ink}}{N/2}}} = e^{\frac{2\pi\;{ink}}{N/2}}}}} & (22)\end{matrix}$As a result, the “second half” of the k values in the N/2 point Fouriertransform can be readily computed.

In DFT, the original sum to get B_(k) involves N multiplications. Theabove analysis breaks the original sum into two sets of sums, each ofwhich involves N/2 multiplications. Now the sums over n are from 0 toN/2−1, instead of being over the even or odds. This allows one to breakthem apart into even and odd terms again in exactly the same way as doneabove (assuming N/2 is also a multiple of 2). This results in four sums,each of which has N/4 terms. If N is a power of 2, the break-downprocess can continue all the way down to 2 point DFT multiplications.

FIG. 6 is a diagram illustrating discrete Fourier Transform (DFT) of avector b=(b₀, b₁, . . . b_(N-1)). The ω_(N) values are multiplied by thenumber on the lower incoming line to each node. At each of the threecolumns in FIG. 6, there are N multiplications, and the number ofcolumns can be divided by 2 before reaching 2, i.e., log(N).Accordingly, the complexity of this DFT is O(N*log N).

The analysis above can be extended beyond the context of DFT as follows.First, a permutation is performed on incoming values in a vector togenerate permutated vector. Permutations are usually O(1) operations.Then, a series of U(2) matrix multiplies is performed on the pairs ofelements of the permuted vector. The U(2) values in the first column ofthe DFT example above are all:

$\begin{matrix}\begin{pmatrix}1 & 1 \\1 & {- 1}\end{pmatrix} & (23)\end{matrix}$

The U(2) matrix multiplication can be performed using other matrices aswell (other than the one shown in (23)). For example, any matrix A∈U(2)⊕U(2) ⊕ . . . ⊕U(2) can be used, where ⊕ designates a direct sum,giving this matrix a block diagonal structure.

The combination of one permutation and one series of U(2) matrixmultiplications can be regarded as one layer as described herein. Theprocess can continue with additional layers, each of which includes onepermutation and multiplications by yet another matrix in U(2) ⊕ . . .⊕U(2). In some embodiments, the layered computations can repeat forabout log(N) times. In some embodiments, the number of layers can be anyother values (e.g., within the available computational power).

The result of the above layered computations includes a matrix of theform:A _(log N) P _(log N) . . . A ₂ P ₂ A ₁ P ₁ b   (24)where A_(i) represents the i_(th) series of matrix multiplications andPi represents the i_(th) permutation in the i_(th) layer.

Because permutations and the A matrices are all unitary, the inverse canalso be readily computed. In the above layered computation, permutationsare computationally free, and the computational cost is from themultiplications in the A_(i) matrices. More specifically, thecomputation includes a total of 2N multiplications in each A_(i), andthere are log(N) of the A_(i) matrices. Accordingly, the computationincludes a total of 2N*log(N), or O(N*log(N)) operations, which arecomparable to the complexity of OFDM.

The layered computation can be applied with any other block-U(m)matrices. For example, the A_(i) matrix can be A_(i)=U(3) ⊕ . . . ⊕U(3)or A_(i)=U(4) ⊕ . . . ⊕U(4). Any other number of m can also be used. Inaddition, any combination of permutations and block-U(m) matrices canalso be used in this layered computation allowable.

In some embodiments, the permutation and the block-U(m) transformationwithin one layer can be performed in a non-consecutive manner. Forexample, after the permutation, any other operations can be performednext before the block-U(m) transformation. In some embodiments, apermutation is not followed by another permutation because permutationsare a closed subgroup of the unitary group. In some embodiments, ablock-U(m) transformation is not followed by another block-U(m)transformation because they also form a closed subgroup of the unitarygroup. In other words, denote B_(n) as a block-U(n) and P aspermutation, then operations like PB_(n″)PB_(n)PB_(n′)B_(n) b andPB_(n′″)B_(n″)B_(n′)PB_(n)Pb can be performed. In contrast, operationslike PB_(n)PPb and B_(n′)PB_(n)B_(n) b can be redundant because twopermutations or two block-U(m) transformations are consecutive here.

The layered approach to construct unitary matrices can also ensure thesecurity of the resulting communication systems. The security of theresulting communication can depend on the size of the matrix space offast unitary matrices compared to the full group U(N).

FIG. 7 is a schematic of a system for communication using layeredconstruction of unitary matrices, according to an embodiment. The system700 includes a plurality of signal transmitters 710(1) to 710 (i)(collectively referred to as transmitters 710) and a plurality of signalreceivers 720(1) to 720(j) (collectively referred to as receivers 720),where i and j are both positive integers. In some embodiments, i and jcan equal. In some other embodiments, i can be different from j. In someembodiments, the transmitters 710 and the receivers 720 are configuredto perform Multiple Input Multiple Output (MIMO) operations.

In some embodiments, each transmitter 710 includes an antenna and thetransmitters 710 can form an antenna array. In some embodiments, eachreceiver includes an antenna and the receivers 720 can also form anantenna array.

The system 700 also includes a processor 730 operably coupled to thesignal transmitters 710. In some embodiments, the processor 730 includesa single processor. In some embodiments, the processor 730 includes agroup of processors. In some embodiments, the processor 730 can beincluded in one or more of the transmitters 710. In some embodiments,the processor 720 can be separate from the transmitters 710. Forexample, the processor 730 can be included in a compute deviceconfigured to process the incoming data 701 and then direct thetransmitters 710 to transmit signals representing the incoming data 701.

The processor 730 is configured to generate a plurality of symbols basedon an incoming data 701 and decompose a unitary transformation matrix ofsize N×N into a set of layers, where N is a positive integer. Each layerincludes a permutation and at least one primitive transformation matrixof size M×M, where M is a positive integer smaller than or equal to N.

The processor 730 is also configured to encode each symbol from theplurality of symbols using at least one layer from the set of layers toproduce a plurality of transformed symbols. A signal representing theplurality of transformed symbols is then sent to the plurality oftransmitters 710 for transmission to the plurality of signal receivers720. In some embodiments, each transmitter in the transmitters 710 cancommunicate with any receiver in the receivers 720.

In some embodiments, the processor 730 is further configured to send asignal representing one of: (1) the unitary transformation matrix, or(2) an inverse of the unitary transformation matrix, to the receivers720, prior to transmission of the signal representing the transformedsymbols to the signal receivers 720. This signal can be used to by thesignal receivers 720 to recover the symbols generated from the inputdata 701. In some embodiments, the unitary transformation matrix can beused for symbol recovery. In some embodiments, the recovery can beachieved by using the inverse of the unitary transformation matrix.

In some embodiments, the fast unitary transformation matrix includes oneof a Fourier matrix, a Walsh matrix, a Haar matrix, a slant matrix, or aToeplitz matrix. In some embodiments, the primitive transformationmatrix has a dimension (e.g., a length) with a magnitude of 2 and theset of layers includes log₂N layers. In some embodiments, any otherlength can be used as described above. In some embodiments, the signalreceivers 720 are configured to transmit a signal representing theplurality of transformed symbols to a target device. Althoughembodiments shown and described herein refer to MIMO systems (e.g.,single-user MIMO systems (SU-MIMO)) having multiple transmitter antennasand multiple receiver antennas, methods set forth herein are alsoapplicable to other systems such as multiple-user MIMO systems (MU-MIMO)which can include a single transmitting antenna but multiple receiverantennas, or multiple transmitting antennas with a single receiverantenna.

Implementations of the various techniques described herein may beimplemented in digital electronic circuitry, or in computer hardware,firmware, software, or in combinations of them. Implementations may beimplemented as a computer program product, i.e., a computer programtangibly embodied in an information carrier, e.g., in a machine-readablestorage device (computer-readable medium, a non-transitorycomputer-readable storage medium, a tangible computer-readable storagemedium, see for example, media 112 and 114 in FIG. 1) or in a propagatedsignal, for processing by, or to control the operation of, dataprocessing apparatus, e.g., a programmable processor, a computer, ormultiple computers. A computer program, such as the computer program(s)described above, can be written in any form of programming language,including compiled or interpreted languages, and can be deployed in anyform, including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment. Acomputer program can be deployed to be processed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network.

Method steps may be performed by one or more programmable processorsexecuting a computer program to perform functions by operating on inputdata and generating output. Method steps also may be performed by, andan apparatus may be implemented as, special purpose logic circuitry,e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the processing of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. Elements of a computer may include atleast one processor for executing instructions and one or more memorydevices for storing instructions and data. Generally, a computer alsomay include, or be operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,e.g., magnetic, magneto-optical disks, or optical disks. Informationcarriers suitable for embodying computer program instructions and datainclude all forms of non-volatile memory, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor andthe memory may be supplemented by, or incorporated in special purposelogic circuitry.

To provide for interaction with a user, implementations may beimplemented on a computer having a display device, e.g., a liquidcrystal display (LCD or LED) monitor, a touchscreen display, fordisplaying information to the user and a keyboard and a pointing device,e.g., a mouse or a trackball, by which the user can provide input to thecomputer. Other kinds of devices can be used to provide for interactionwith a user as well; for example, feedback provided to the user can beany form of sensory feedback, e.g., visual feedback, auditory feedback,or tactile feedback; and input from the user can be received in anyform, including acoustic, speech, or tactile input.

Implementations may be implemented in a computing system that includes aback-end component, e.g., as a data server, or that includes amiddleware component, e.g., an application server, or that includes afront-end component, e.g., a client computer having a graphical userinterface or a Web browser through which a user can interact with animplementation, or any combination of such back-end, middleware, orfront-end components. Components may be interconnected by any form ormedium of digital data communication, e.g., a communication network.Examples of communication networks include a local area network (LAN)and a wide area network (WAN), e.g., the Internet.

While certain features of the described implementations have beenillustrated as described herein, many modifications, substitutions,changes and equivalents will now occur to those skilled in the art. Itis, therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the scope of theimplementations. It should be understood that they have been presentedby way of example only, not limitation, and various changes in form anddetails may be made. Any portion of the apparatus and/or methodsdescribed herein may be combined in any combination, except mutuallyexclusive combinations. The implementations described herein can includevarious combinations and/or sub-combinations of the functions,components and/or features of the different implementations described.

The invention claimed is:
 1. An apparatus, comprising: a firstcommunication device including a plurality of antennas and configured toaccess a codebook of unitary matrices that is also accessible by asecond communication device; and a processor operatively coupled to thefirst communication device, the processor configured to: receive aplurality of symbols; apply, to each symbol from the plurality ofsymbols, at least one layer from an associated unitary matrix from aplurality of unitary matrices of the codebook of unitary matrices, togenerate a plurality of transformed symbols; apply, to each transformedsymbol from the plurality of transformed symbols, a precode matrixselected from a plurality of precode matrices of the codebook of unitarymatrices, to produce a plurality of precoded symbols; and send a signalto cause transmission, to the second communication device, of aplurality of signals, each signal from the plurality of signalsrepresenting a precoded symbol from the plurality of precoded symbols,each signal from the plurality of signals transmitted using a uniqueantenna from the plurality of antennas.
 2. The apparatus of claim 1,wherein: the plurality of antennas is a first plurality of antennas, andthe second communication device includes a second plurality of antennas,the first communication device and the second communication deviceconfigured to perform multiple-in multiple-out (MIMO) operations, thefirst plurality of antennas including T antennas and the secondplurality of antennas including R antennas, the MIMO operations havingan associated computational cost of O(T log₂ T) arithmetic operationsfor the first communication device, and the MIMO operations having anassociated computational cost of O(R log₂ R) arithmetic operations forthe second communication device.
 3. The apparatus of claim 1, whereinthe plurality of antennas is a first plurality of antennas and thesecond communication device includes a second plurality of antennas, thefirst communication device and the second communication deviceconfigured to perform MIMO operations.
 4. The apparatus of claim 1,wherein the signal to cause transmission of the plurality of signalsdoes not cause transmission of any precode matrix from the plurality ofprecode matrices.
 5. The apparatus of claim 1, wherein the precodematrix selected from the codebook of unitary matrices includes at leastone precode matrix selected from the codebook of unitary matricespseudo-randomly.
 6. The apparatus of claim 1, wherein the codebook ofunitary matrices does not include a frequency-domain transformation. 7.The apparatus of claim 1, wherein the codebook of unitary matrices doesnot include a time-domain transformation.
 8. A method, comprising:receiving, at a plurality of antennas of a communication device and viaa communication channel, a plurality of signals, each signal from theplurality of signals representing transformed symbols from a firstplurality of transformed symbols; identifying a left singular vector ofthe communication channel and a right singular vector of thecommunication channel; removing the left singular vector and the rightsingular vector from the first plurality of transformed symbols togenerate a second plurality of transformed symbols; and identifying atleast one message associated with the plurality of signals by querying acodebook of transformation matrices based on the second plurality oftransformed symbols.
 9. The method of claim 8, further comprisingdecomposing a unitary transformation matrix into a plurality of layersto produce the codebook of transformation matrices, each layer from theplurality of layers including a permutation and a primitivetransformation matrix.
 10. The method of claim 8, wherein: the pluralityof antennas is a first plurality of antennas, and the secondcommunication device includes a second plurality of antennas, the firstcommunication device and the second communication device configured toperform MIMO operations, the first plurality of antennas including Rantennas and the second plurality of antennas including T antennas, theMIMO operations having an associated computational cost of O(R log₂ R)arithmetic operations for the first communication device, and the MIMOoperations having an associated computational cost of O(T log₂ T)arithmetic operations for the second communication device.
 11. Themethod of claim 8, wherein the plurality of antennas is a firstplurality of antennas and the second communication device includes asecond plurality of antennas, the first communication device and thesecond communication device configured to perform MIMO operations. 12.The method of claim 8, wherein the codebook of transformation matricesdoes not include a frequency-domain transformation or a time-domaintransformation.
 13. The method of claim 8, wherein the codebook oftransformation matrices does not include a frequency-domaintransformation.
 14. The method of claim 8, wherein the codebook oftransformation matrices does not include a time-domain transformation.15. A non-transitory, processor-readable medium storing instructions tocause a processor to: receive a plurality of symbols; apply, to eachsymbol from the plurality of symbols, at least one layer associated witha codebook of unitary matrices, to generate a plurality of transformedsymbols; apply, to each transformed symbol from the plurality oftransformed symbols, a precode matrix selected from a plurality ofprecode matrices of the codebook of unitary matrices, to produce aplurality of precoded symbols; and send a signal to cause transmission,to a communication device, of a plurality of signals, each signal fromthe plurality of signals representing a precoded symbol from theplurality of precoded symbols, each signal from the plurality of signalstransmitted using an antenna different from remaining antennas from aplurality of antennas.
 16. The non-transitory, processor-readable mediumof claim 15, wherein the communication device is configured to performMIMO operations.
 17. The non-transitory, processor-readable medium ofclaim 15, wherein the signal to cause transmission of the plurality ofsignals does not cause transmission of any precode matrix from theplurality of precode matrices.
 18. The non-transitory,processor-readable medium of claim 15, wherein the precode matrix isselected from the codebook of unitary matrices pseudo-randomly.
 19. Thenon-transitory, processor-readable medium of claim 15, wherein thecodebook of unitary matrices does not include a frequency-domaintransformation.
 20. The non-transitory, processor-readable medium ofclaim 15, wherein the codebook of unitary matrices does not include atime-domain transformation.